{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>薪资</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>少儿编程讲师-Java/python</td>\n",
       "      <td>0.8-1.2万/月</td>\n",
       "      <td>在校生/应届生</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Python开发工程师</td>\n",
       "      <td>0.8-1.2万/月</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>python开发工程师</td>\n",
       "      <td>1.2-1.6万/月</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python教研/教学主任</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>德企招Python 全栈工程师</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>少儿编程讲师-java/web/python</td>\n",
       "      <td>0.8-1万/月</td>\n",
       "      <td>在校生/应届生</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>计算机视觉算法工程师（python）(002083)</td>\n",
       "      <td>1.5-2万/月</td>\n",
       "      <td>无需经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>运维工程师（驻场银行）</td>\n",
       "      <td>0.9-1.3万/月</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>测试工程师</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>量化投资研究员</td>\n",
       "      <td>1-2万/月</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>本科</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            职位          薪资     工作经验 学历要求\n",
       "0           少儿编程讲师-Java/python  0.8-1.2万/月  在校生/应届生   本科\n",
       "1                  Python开发工程师  0.8-1.2万/月     1年经验   本科\n",
       "2                  python开发工程师  1.2-1.6万/月     1年经验   本科\n",
       "3                Python教研/教学主任    1-1.5万/月   3-4年经验   本科\n",
       "4              德企招Python 全栈工程师    1-1.5万/月     2年经验   本科\n",
       "..                         ...         ...      ...  ...\n",
       "95      少儿编程讲师-java/web/python    0.8-1万/月  在校生/应届生   本科\n",
       "96  计算机视觉算法工程师（python）(002083)    1.5-2万/月     无需经验   本科\n",
       "97                 运维工程师（驻场银行）  0.9-1.3万/月     2年经验   本科\n",
       "98                       测试工程师    1-1.5万/月   3-4年经验   本科\n",
       "99                     量化投资研究员      1-2万/月     2年经验   本科\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"./python_job.txt\", sep=\"\\t\")\n",
    "df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_xinzi(x):\n",
    "    if \"万/月\" in x: \n",
    "        low,high = x.replace(\"万/月\", \"\").split(\"-\")\n",
    "        return 10000*(float(high)+float(low))/2\n",
    "    if \"千/月\" in x: \n",
    "        low,high = x.replace(\"千/月\", \"\").split(\"-\")\n",
    "        return 1000*(float(high)+float(low))/2\n",
    "    if \"万/年\" in x: \n",
    "        low,high = x.replace(\"万/年\", \"\").split(\"-\")\n",
    "        return 10000*(float(high)+float(low))/2/12\n",
    "df[\"薪资2\"] = df[\"薪资\"].map(compute_xinzi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职位</th>\n",
       "      <th>薪资</th>\n",
       "      <th>工作经验</th>\n",
       "      <th>学历要求</th>\n",
       "      <th>薪资2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>少儿编程讲师-Java/python</td>\n",
       "      <td>0.8-1.2万/月</td>\n",
       "      <td>在校生/应届生</td>\n",
       "      <td>本科</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Python开发工程师</td>\n",
       "      <td>0.8-1.2万/月</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>本科</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>python开发工程师</td>\n",
       "      <td>1.2-1.6万/月</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>本科</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Python教研/教学主任</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>本科</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>德企招Python 全栈工程师</td>\n",
       "      <td>1-1.5万/月</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>本科</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   职位          薪资     工作经验 学历要求     薪资2\n",
       "0  少儿编程讲师-Java/python  0.8-1.2万/月  在校生/应届生   本科  2000.0\n",
       "1         Python开发工程师  0.8-1.2万/月     1年经验   本科  2000.0\n",
       "2         python开发工程师  1.2-1.6万/月     1年经验   本科  2000.0\n",
       "3       Python教研/教学主任    1-1.5万/月   3-4年经验   本科  2500.0\n",
       "4     德企招Python 全栈工程师    1-1.5万/月     2年经验   本科  2500.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9915ed810>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 19975 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26376 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 21315 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20197 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 19979 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 22825 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 19978 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 23567 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26102 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 19975 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26376 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 21315 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20197 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 19979 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20803 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 22825 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 19978 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 23567 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26102 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 34218 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 36164 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 34218 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/peishuaishuai/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 36164 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"薪资\").size().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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